Enhancing Cycling Safety at Signalized Intersections: Analysis of Observed Behavior
Bibliographic record
Abstract
Urban transportation systems tend to operate most effectively when common expectations exist about all user travel behavior under various conditions. A wide range of behavior among cyclists presents a significant challenge to the achievement of safer and improved designs at intersections. In this research, cyclists were observed (i.e., through the use of video at fixed-camera locations) as they made left turns at six intersections in Toronto, Ontario, Canada. The intersections were classified into five types on the basis of their physical designs and operational characteristics. Cyclist behavior was assessed to determine the propensity to traverse the intersection legally, designated as “rule compliance.” Further, the analysis determined the likelihood that a cyclist would traverse an intersection in a path that was consistent with the design; this outcome was defined as “facility compliance.” The results revealed that the presence of bike boxes, two-phase lefts, and turning lanes with advanced green phases positively influenced cyclists by increasing the likelihood that left turns would be legal and consistent with the behavior intended through the design. The results also suggested that the highest rates of rule and facility compliance existed under the condition in which cyclists approached an intersection during a green signal. On the basis of the observations in the research, design recommendations were made to accommodate cyclists better and produce more consistent behavior and presumably to enhance safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".